Validating the Literature
Validating the Literature
A verification of COVID-19's long-term effects through anomaly detection
Validating the Literature
A verification of COVID-19's long-term effects through anomaly detection
Devin Lim, Ponnu Soman, Duong Le, Lakshit GuptaBackground
Background
COVID reported death-toll: ~1,200,000[1]
Background
COVID reported death-toll: ~1,200,000[1]
Background
COVID reported death-toll: ~1,200,000[1]
β But wait, there's more!
What the Literature Says
What the Literature Says
- Measurable effects long after the initial infection
- Damages every part of the body
- Brain
- Lungs
- Heart
- Kidneys
- And more!
β How?
Mechanism
Mechanism
- Damage to the mitochondria
Mechanism
- Damage to the mitochondria β nectroptosis
Mechanism
- Damage to the mitochondria β nectroptosis β organ damage[2]
Mechanism
- Damage to the mitochondria β nectroptosis β organ damage[2]
- Excessive immune response
Mechanism
- Damage to the mitochondria β nectroptosis β organ damage[2]
- Excessive immune response β inflammation & blood clots
Mechanism
- Damage to the mitochondria β nectroptosis β organ damage[2]
- Excessive immune response β inflammation & blood clots β more organ damage[3]
Mechanism
- Damage to the mitochondria β nectroptosis β organ damage[2]
- Excessive immune response β inflammation & blood clots β more organ damage[3]
β Seems... worrying
The Big Question:
The Big Question:
Are the claims true?
Methodology
Methodology
A little revisit:
Methodology
A little revisit:
Is this accurate?
How is it counted anyway?
Death Certificates
Death Certificates
Any mention of COVID == 1 count
Problems?
Problems?
- Reporting Lag
- Human Error
- Indirect, delayed deaths
- Inconsistency between jurisdictions
- Limited testing capacity, especially early in the pandemic
- And so on...
β Underreporting!
Solution?
Solution?
Excess Mortality
Excess Mortality
Idea: Death rate is fairly constant year-over-year
Excess Mortality
Idea: Death rate is fairly constant year-over-year
Excess Mortality
Steps:
- Ignore reported count
- Calculate expected deaths
- Compare with actual deaths
Result:
- A sudden, drastic increase in deaths
- Pattern follows reported deaths
- An undercount of ~200,000!
β Can we do the same with other diseases?
Data Methodology
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Chronic Health Indicators
Behavioral Risk Factor Surveillance System
- Respiratory diseases
- Cardiovascular diseases
- Depression
- Diabetes
- Kidney
- Cancer
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Chronic Health Indicators
Behavioral Risk Factor Surveillance System
- Respiratory diseases
- Cardiovascular diseases
- Depression
- Diabetes
- Kidney
- Cancer
Prevalence of Disability Status and Types
Disability and Health Data System
- Overall disability rates
- Breakdown by disability types
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Chronic Health Indicators
Behavioral Risk Factor Surveillance System
- Respiratory diseases
- Cardiovascular diseases
- Depression
- Diabetes
- Kidney
- Cancer
Prevalence of Disability Status and Types
Disability and Health Data System
- Overall disability rates
- Breakdown by disability types
Daily COVID-19 Vaccines Administered
Our World in Data + CDC
- Vaccines
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Chronic Health Indicators
Behavioral Risk Factor Surveillance System
- Respiratory diseases
- Cardiovascular diseases
- Depression
- Diabetes
- Kidney
- Cancer
Prevalence of Disability Status and Types
Disability and Health Data System
- Overall disability rates
- Breakdown by disability types
Daily COVID-19 Vaccines Administered
Our World in Data + CDC
- Vaccines
Deaths Involving Coronavirus Disease
Department of Health & Human Services
- COVID death-toll
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Standard stuff: filtering, renaming, grouping, sorting
brfss_filters = {
"Locationabbr": ["UW"],
"Topic": [
"Asthma",
"COPD",
...
],
"Response": ["Yes"],
}
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Standard stuff: filtering, renaming, grouping, sorting
brfss_filters = {
"Locationabbr": ["UW"],
"Topic": [
"Asthma",
"COPD",
...
],
"Response": ["Yes"],
}
brfss_col_renames = {
"Locationabbr": "LocationAbbr",
"Data_value": "DataValue",
...
}
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Standard stuff: filtering, renaming, grouping, sorting
brfss_filters = {
"Locationabbr": ["UW"],
"Topic": [
"Asthma",
"COPD",
...
],
"Response": ["Yes"],
}
brfss_col_renames = {
"Locationabbr": "LocationAbbr",
"Data_value": "DataValue",
...
}
brfss_val_renames = {
"Question": {
"Adults who have been told they currently have asthma (variable calculated from one or more BRFSS questions)": "Current Asthma",
"Ever told you had angina or coronary heart disease?": "Coronary Heart Disease",
...
},
"Topic": {
"Asthma": "Respiratory Diseases",
"COPD": "Respiratory Diseases",
"Other Cancer": "Cancer",
},
}
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Spline
Data Methodology
Data Sourcing
Data Cleaning
Prediction Methods
Spline
Time Series
Chronic Health Indicators
JACI: There is evidence that COVID-19 increases the risk of developing asthma
We were not able to find evidence of a causal link between COVID-19 and COPD
American Heart Association: There is evidence that COVID-19 causes lasting heart damage
Yale: There is strong evidence of significant increases in brain-related symptoms after a COVID-19 infection. Depression is one such symptom.
Cedars-Sinai Medical Center: There is strong evidence that COVID-19 increases the risk of new-onset (type 2) diabetes
Nature: There is strong evidence that COVID-19 increases the risk of CKD
Yale: Confirmed the evidence, but also found that CKD that developed due to COVID-19 recovered faster
We found mixed literature on the causal link between COVID-19 and Cancer
Disability
Vaccine
ConclusionΒΆ
- Vaccine works!
- All time highs for:
- Disability
- CKD
- Diabetes
- Depression
- Asthma
- Non-skin cancers
- Agreed with the literature on:
- CKD
- Disability
- Diabetes
- Depression
- Asthma